For the newbys.... how to read in data... simple

R-Graph Gallery

Here some really great examples of graphics in R.  They even publish their code they used to great these graphs.
http://addictedtor.free.fr/graphiques/

Random Forests

Are you interested in decision trees or classification trees?  Well then you should check out this package in R http://www.stat.berkeley.edu/~breiman/RandomForests/

There are so many applications of this package, and you can also download and compile in C!  This site has great documentation and there is also some information on the Quick-R site.

Quick R

Quick R is a great place to learn about R functions and graphics!  I use it all the time and recommend it for users of all levels!

BioConductor

http://www.bioconductor.org/

Bioconductor is an open source and open development software project
for the analysis and comprehension of genomic data.

The broad goals of the Bioconductor project are:

• To provide widespread access to a broad range of powerful statistical and graphical methods for the analysis of genomic data.
• To facilitate the inclusion of biological metadata in the analysis of genomic data, e.g. literature data from PubMed, annotation data from LocusLink.
• To provide a common software platform that enables the rapid development and deployment of extensible, scalable, and interoperable software.
• To further scientific understanding by producing high-quality documentation and reproducible research.
• To train researchers on computational and statistical methods for the analysis of genomic data.

Purdue R-users Group

This will become the blogosphere for the Purdue University R-users Group. This group will allow for networking between R-users from all disciplines across campus.

From the SF area R-users group: "R is an open source programming language for statistical computing, data analysis, and graphical visualization. R has an estimated one million users worldwide, and its user base is growing. While most commonly used within academia, in fields such as computational biology and applied statistics, it is gaining currency in commercial areas such as quantitative finance and business intelligence.

Among R's strengths as a language are its powerful built-in tools for inferential statistics, its compact modeling syntax, its data visualization capabilities, and its ease of connectivity with persistent data stores (from databases to flatfiles).

In addition, R's open source nature and its extensibility via add-on "packages" has allowed it to keep up with the leading edge in academic research.

For all its strengths, though, R has an admittedly steep learning curve; the first steps towards learning and using R can be challenging.

To this end, the Bay Area R Users Group is dedicated to bringing together area practitioners of R to exchange knowledge, inspire new users, and spur the adoption of R for innovative research and commercial applications."